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Turn a useful question into a prediction task.
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ML 101. Data, features and labels. Turn a useful question into a prediction task. Define the prediction first. Machine learning fits a model to patterns in data. Begin with the decision you want to support: predicting a category, estimating a number or finding a useful representation. In supervised learning, examples include a target answer called a label. Classification predicts a category; regression predicts a numerical quantity. Neither task guarantees that a model has discovered the cause of what it predicts. Give each example a clear meaning. Features are the inputs available to the model, such as measurements or pixels. A label is the target associated with an example. For a teaching exercise, imagine photographs labeled “needs review” or “routine.” Specify who assigns those labels and what each category means. Inconsistent labeling teaches inconsistent targets. An annotation is a recorded description or label; it is not automatically objective truth. Think about where the data came from. A dataset made entirely from bright indoor photographs may not represent outdoor field use. Ask which devices, locations and conditions are included, which are missing and whether you have permission to use the material. Keep relevant context such as capture session or site identity so you can later split and evaluate examples sensibly. More examples help only when they are suitable for the task and collected under an appropriate process. HerbDev example. The case study connects capture, review, datasets and evaluation. For this course, the “needs review” classifier is a hypothetical teaching exercise inspired by that workflow; it is not a reported OrganicVision model or performance result. The process: Define a prediction. Then Identify available features. Then Define the target labels. Then Inspect coverage and quality. Try this. For a photo review classifier, identify the input, target and one label ambiguity. Pause the audio to try the exercise. A worked answer and checkpoint questions are available on the lesson page.
Define the prediction first
Machine learning fits a model to patterns in data. Begin with the decision you want to support: predicting a category, estimating a number or finding a useful representation. In supervised learning, examples include a target answer called a label. Classification predicts a category; regression predicts a numerical quantity. Neither task guarantees that a model has discovered the cause of what it predicts.
Give each example a clear meaning
Features are the inputs available to the model, such as measurements or pixels. A label is the target associated with an example. For a teaching exercise, imagine photographs labeled “needs review” or “routine.” Specify who assigns those labels and what each category means. Inconsistent labeling teaches inconsistent targets. An annotation is a recorded description or label; it is not automatically objective truth.
Think about where the data came from
A dataset made entirely from bright indoor photographs may not represent outdoor field use. Ask which devices, locations and conditions are included, which are missing and whether you have permission to use the material. Keep relevant context such as capture session or site identity so you can later split and evaluate examples sensibly. More examples help only when they are suitable for the task and collected under an appropriate process.
- Define a prediction
- Identify available features
- Define the target labels
- Inspect coverage and quality
Try it yourself
For a photo review classifier, identify the input, target and one label ambiguity.
Compare with a worked answer
Input: a photograph. Target: whether it should enter a review queue under a written review policy. Ambiguity: one reviewer flags blur while another does not. Agree on the policy before interpreting a model’s errors.
Check your understanding
Choose one answer for each question, then check your score. Read the explanations and retry any question you missed.
Read the answer guide
1. The target associated with a training example A label supplies the target the model is trained to predict.
2. Regression Regression predicts a numerical quantity; classification predicts a category.
3. They make the target the model should learn unclear The model cannot reliably learn a clearly defined policy if the examples represent conflicting policies.
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You have answered every checkpoint correctly. Use your worked exercises to explain the ideas to someone else, then apply them to a small project.
Apply it in the final project · Choose what to learn next →Keep exploring: What next? · AI vocabulary · AI 101 · Learning hub